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Record W2419477531 · doi:10.1055/s-0035-1549224

Subdural Metastasis of Prostate Cancer

2015· article· en· W2419477531 on OpenAlexaff
A. Nzokou, Elsa Magro, F Guilbert, Jean–Yves Fournier, Michel W. Bojanowski

Bibliographic record

VenueJournal of Neurological Surgery Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineMetastasisProstate cancerRadiologyProstateBone metastasisHematomaAdenocarcinomaDifferential diagnosisBrain metastasisNeurosurgeryCancerLesionNodule (geology)SurgeryPathologyInternal medicine

Abstract

fetched live from OpenAlex

Dural metastasis from prostate cancer is rare and may mimic a subdural hematoma (SDH). Preoperatively diagnosis may be difficult and only reveal its presence during surgery. We present such a case and review the literature to identify common characteristics. A 65-year-old man presented with headache, confusion, and progressive right upper limb weakness. Past history included a prostate adenocarcinoma with bone metastasis 3 years earlier. Head computed tomography (CT) scan without contrast revealed a multinodular bilateral hyperdense extra-axial lesion interpreted as acute SDH. At surgery planned for SDH drainage no blood was found; instead there was an en plaque subdural yellowish tumor. Histopathologic examination was consistent with metastatic adenocarcinoma of the prostate. We found 11 cases reported as dural metastasis of prostate cancer mimicking SDH. Surgery was performed on nine cases with no suspicion of dural metastasis. On preoperative nonenhanced CT scan images, three types of image patterns can be described: a nodule in SDH, multinodular metastasis surrounded by SDH, and large en plaque subdural tumor. The latter group consists of those cases where no blood but rather an en plaque subdural tumor was found at surgery. Even though rare, dural metastasis should be considered among the differential diagnoses in a patient known for prostate cancer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.090
GPT teacher head0.321
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2015
Admission routes1
Has abstractyes

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